… set reductions and neural signatures associated with different emotions according to the …
deep neural networks. In Section VI, we discuss the problems in emotion recognition studies. …

… These statistical features were used as input of emotional recognition based on CNN (…
In the case of the above problems, this paper presents a nova emotion recognition algorithm …

… In this paper, we propose a regularized graph neural network for EEG-based emotion
recognition. Our model is inspired by neuroscience theories on human brain organization and cap…

… - Speech Emotion Recognition ( … emotional speech in order to design models for auto - SER
architecture based on Convolutional Neural Networks mated recognition of spoken emotions …

… We present a deep convolutional recurrent neural network for speech emotion recognition
… of emotion expressed in speech. The experimental results on the eNTERFACE’05 emotion …

… and especially deep neural network can learn … emotion recognition. The proposed framework
uses the Gabor filters for feature extraction and then a Convolutional Neural Network (CNN…

… After that, we feed the fusion emotion feature vector V into a deep neural network (DNN)
network containing three dense layers, in which the parameters are set as 1024, 512, and 4 and …

… the physiological data and recognize emotion signature which is reflected in … Neural Networks
(RNNs) for human emotion recognition has led to a significant improvement in recognition …

… for emotion recognition using information of the pupil. In last years, the pupil variables have
been used as an assessment of emotional … The emotions are provoked by visual stimuli of …

… human emotions are not isolating. However, most existing emotion recognition methods based
on … In this paper, a new emotion recognition method is proposed to extract spatiotemporal …